Cell-to-cell variation sets a tissue-rheology-dependent bound on collective gradient sensing

When a single cell senses a chemical gradient and chemotaxes, stochastic receptor-ligand binding can be a fundamental limit to the cell's accuracy. For clusters of cells responding to gradients, however, there is a critical difference: even genetically identical cells have differing responses to chemical signals. With theory and simulation, we show collective chemotaxis is limited by cell-to-cell variation in signaling. We find that when different cells cooperate the resulting bias can be much larger than the effects of ligand-receptor binding. Specifically, when a strongly-responding cell is at one end of a cell cluster, cluster motion is biased toward that cell. These errors are mitigated if clusters average measurements over times long enough for cells to rearrange. In consequence, fluid clusters are better able to sense gradients: we derive a link between cluster accuracy, cell-to-cell variation, and the cluster rheology. Because of this connection, increasing the noisiness of individual cell motion can actually increase the collective accuracy of a cluster by improving fluidity

Emergent collective chemotaxis without single-cell gradient sensing

Many eukaryotic cells chemotax, sensing and following chemical gradients. However, experiments have shown that even under conditions when single cells cannot chemotax, small clusters may still follow a gradient. This behavior has been observed in neural crest cells, in lymphocytes, and during border cell migration in Drosophila, but its origin remains puzzling. Here, we propose a new mechanism underlying this "collective guidance", and study a model based on this mechanism both analytically and computationally. Our approach posits that the contact inhibition of locomotion (CIL), where cells polarize away from cell-cell contact, is regulated by the chemoattractant. Individual cells must measure the mean attractant value, but need not measure its gradient, to give rise to directional motility for a cell cluster. We present analytic formulas for how cluster velocity and chemotactic index depend on the number and organization of cells in the cluster. The presence of strong orientation effects provides a simple test for our theory of collective guidance.Comment: Updated with additional simulations. Aspects of v1 of this paper about adaptation and amplification have been extended and turned into a separate paper, and removed from the current versio

Collective signal processing in cluster chemotaxis: roles of adaptation, amplification, and co-attraction in collective guidance

Single eukaryotic cells commonly sense and follow chemical gradients, performing chemotaxis. Recent experiments and theories, however, show that even when single cells do not chemotax, clusters of cells may, if their interactions are regulated by the chemoattractant. We study this general mechanism of "collective guidance" computationally with models that integrate stochastic dynamics for individual cells with biochemical reactions within the cells, and diffusion of chemical signals between the cells. We show that if clusters of cells use the well-known local excitation, global inhibition (LEGI) mechanism to sense chemoattractant gradients, the speed of the cell cluster becomes non-monotonic in the cluster's size - clusters either larger or smaller than an optimal size will have lower speed. We argue that the cell cluster speed is a crucial readout of how the cluster processes chemotactic signal; both amplification and adaptation will alter the behavior of cluster speed as a function of size. We also show that, contrary to the assumptions of earlier theories, collective guidance does not require persistent cell-cell contacts and strong short range adhesion to function. If cell-cell adhesion is absent, and the cluster cohesion is instead provided by a co-attraction mechanism, e.g. chemotaxis toward a secreted molecule, collective guidance may still function. However, new behaviors, such as cluster rotation, may also appear in this case. Together, the combination of co-attraction and adaptation allows for collective guidance that is robust to varying chemoattractant concentrations while not requiring strong cell-cell adhesion.Comment: This article extends some results previously presented in arXiv:1506.0669

Periodic migration in a physical model of cells on micropatterns

We extend a model for the morphology and dynamics of a crawling eukaryotic cell to describe cells on micropatterned substrates. This model couples cell morphology, adhesion, and cytoskeletal flow in response to active stresses induced by actin and myosin. We propose that protrusive stresses are only generated where the cell adheres, leading to the cell's effective confinement to the pattern. Consistent with experimental results, simulated cells exhibit a broad range of behaviors, including steady motion, turning, bipedal motion, and periodic migration, in which the cell crawls persistently in one direction before reversing periodically. We show that periodic motion emerges naturally from the coupling of cell polarization to cell shape by reducing the model to a simplified one-dimensional form that can be understood analytically.Comment: 15 pages (includes supplementary material as an appendix). Recently accepted to Physical Review Letter

Tunable Transient Decay Times in Nonlinear Systems: Application to Magnetic Precession

The dynamical motion of the magnetization plays a key role in the properties of magnetic materials. If the magnetization is initially away from the equilibrium direction in a magnetic nanoparticle, it will precess at a natural frequency and, with some damping present, will decay to the equilibrium position in a short lifetime. Here we investigate a simple but important situation where a magnetic nanoparticle is driven non-resonantly by an oscillating magnetic field, not at the natural frequency. We find a surprising result that the lifetime of the transient motion is strongly tunable, by factors of over 10,000, by varying the amplitude of the driving field.Comment: EPL Preprin

Mobile Identity, Credential, and Access Management Framework

Organizations today gather unprecedented quantities of data from their operations. This data is coming from transactions made by a person or from a connected system/application. From personal devices to industry including government, the internet has become the primary means of modern communication, further increasing the need for a method to track and secure these devices. Protecting the integrity of connected devices collecting data is critical to ensure the trustworthiness of the system. An organization must not only know the identity of the users on their networks and have the capability of tracing the actions performed by a user but they must trust the system providing them with this knowledge. This increase in the pace of usage of personal devices along with a lack of trust in the internet has driven demand for trusted digital identities. As the world becomes increasingly mobile with the number of smart phone users growing annually and the mobile web flourishing, it is critical to implement strong security on mobile devices. To manage the vast number of devices and feel confident that a machine’s identity is verifiable, companies need to deploy digital credentialing systems with a strong root of trust. As passwords are not a secure method of authentication, mobile devices and other forms of IoT require a means of two-factor authentication that meets NIST standards. Traditionally, this has been done with Public Key Infrastructure (PKI) through the use of a smart card. Blockchain technologies combined with PKI can be utilized in such a way as to provide an identity and access management solution for the internet of things (IoT). Improvements to the security of Radio Frequency Identification (RFID) technology and various implementations of blockchain make viable options for managing the identity and access of IoT devices. When PKI first began over two decades ago, it required the use of a smart card with a set of credentials known as the personal identity verification (PIV) card. The PIV card (something you have) along with a personal identification number (PIN) (something you know) were used to implement two-factor authentication. Over time the use of the PIV cards has proven challenging as mobile devices lack the integrated smart card readers found in laptop and desktop computers. Near Field Communication (NFC) capability in most smart phones and mobile devices provides a mechanism to allow a PIV card to be read by a mobile device. In addition, the existing PKI system must be updated to meet the demands of a mobile focused internet. Blockchain technology is the key to modernizing PKI. Together, blockchain-based PKI and NFC will provide an IoT solution that will allow industry, government, and individuals a foundation of trust in the world wide web that is lacking today

Magnetic anisotropy of FePt nanoparticles

We carry out a systematic theoretical investigation of Magneto Crystalline Anisotropy (MCA) of L10 FePt clusters with alternating Fe and Pt planes along the (001) direction. We calculate the structural relaxation and magnetic moment of each cluster by using ab initio spin-polarized density functional theory (DFT), and the MCA with both spin-polarized DFT (including spin-orbit coupling self-consistently) and the torque method. We find that the MCA of any composite structure of a given size is enhanced with respect to that of the same-sized pure Pt or pure Fe cluster as well as to that of any pair of Fe and Pt atoms in bulk L10 FePt. This enhancement results from the hybridization we observe between the 3d orbital of the Fe atoms and the 5d orbital of their Pt neighbors. This hybridization, however, affects the electronic properties of the component atoms in significantly different ways. While it somewhat increases the spin moment of the Fe atoms, it has little effect on their orbital moment; at the same time, it greatly increases both the spin and orbital moment of the Pt atoms. Given the fact that the spin-orbit coupling (SOC) constant of Pt is about 7 times greater than that of Fe, this Fe-induced jump in the orbital moment of the Pt atoms produces the increase in MCA of the composite structures over that of their pure counterparts. That any composite structure exhibits higher MCA than bulk L10 FePt results from the lower coordination of Pt atoms in the cluster, whether Fe or Pt predominates within it. We also find that bipyramidal clusters whose central layer is Pt have higher MCA than their same-sized counterparts whose central layer is Fe. This results from the fact that Pt atoms in such configurations are coordinated with more Fe atoms than in the latter. By thus participating in more instances of hybridization, they contribute higher orbital moments to the overall MCA of the unit

Nonlinear dynamics of confined cell migration -- modeling and inference

The motility of eukaryotic cells is strongly influenced by their environment, with confined cells often developing qualitatively different motility patterns from those migrating on simple two-dimensional substrates. Recent experiments, coupled with data-driven methods to extract a cell's equation of motion, showed that cancerous MDA-MB-231 cells persistently hop in a limit cycle when placed on two-state adhesive micropatterns (two large squares connected by a narrow bridge), while they remain stationary on average in rectangular confinements. In contrast, healthy MCF10A cells migrating on the two-state micropattern are bistable, i.e., they settle into either basin on average with only noise-induced hops between the two states. We can capture all these behaviors with a single computational phase field model of a crawling cell, under the assumption that contact with non-adhesive substrate inhibits the cell front. Our model predicts that larger and softer cells are more likely to persistently hop, while smaller and stiffer cells are more likely to be bistable. Other key factors controlling cell migration are the frequency of protrusions and their magnitude of noise. Our results show that relatively simple assumptions about how cells sense their geometry can explain a wide variety of different cell behaviors, and show the power of data-driven approaches to characterize both experiment and simulation

Limits on the accuracy of contact inhibition of locomotion

Cells that collide with each other repolarize away from contact, in a process called contact inhibition of locomotion (CIL), which is necessary for correct development of the embryo. CIL can occur even when cells make a micron-scale contact with a neighbor - much smaller than their size. How precisely can a cell sense cell-cell contact and repolarize in the correct direction? What factors control whether a cell recognizes it has contacted a neighbor? We propose a theoretical model for the limits of CIL where cells recognize the presence of another cell by binding the protein ephrin with the Eph receptor. This recognition is made difficult by the presence of interfering ligands that bind nonspecifically. Both theoretical predictions and simulation results show that it becomes more difficult to sense cell-cell contact when it is difficult to distinguish ephrin from the interfering ligands, or when there are more interfering ligands, or when the contact width decreases. However, the error of estimating contact position remains almost constant when the contact width changes. This happens because the cell gains spatial information largely from the boundaries of cell-cell contact. We study using statistical decision theory the likelihood of a false positive CIL event in the absence of cell-cell contact, and the likelihood of a false negative where CIL does not occur when another cell is present. Our results suggest that the cell is more likely to make incorrect decisions when the contact width is very small or so large that it nears the cell's perimeter. However, in general, we find that cells have the ability to make reasonably reliable CIL decisions even for very narrow (micron-scale) contacts, even if the concentration of interfering ligands is ten times that of the correct ligands